Computational Solutions to Exosomal microRNA Biomarker Detection in Pancreatic Cancer

Pancreatic cancer is the fourth leading cause of cancer death in the United States and the 5-year survival rate is only 5% to 10%. There are only a few non-specific symptoms associated with the early-stage cancer, therefore most patients are diagnosed in a late stage. Due to the lack of effective treatments and the fact that the early stage has a 39% 5-year survival rate, the biggest hope to control this disease is early detection. Therefore, discovery of effective and reliable non-invasive biomarkers for early detection of pancreatic cancer has been a major topic. Very recently, exosomal microRNAs have become promising candidates of diagnostic markers due to the facts that 1) such small non-coding RNA are stably present in the tissue and can get into blood circulation via exosome packaging which protects them from enzymatic degradation; 2) cancer cells, even at their early stages, may secrete up to tenfold more exosomes than normal cells and some disease-associated microRNAs can get into blood stream; 3) circulating exosomal microRNAs may carry early signals of cancers. With the goal to facilitate cancer detection, in this study, we have developed an integrated computational approach that leverages advanced genomics and bioinformatics to identify exosomal microRNAs that can be promising early detection biomarkers in pancreatic cancer. Our study has presented a new data-driven strategy that can potentially advance the biomedical research in biomarker discovery. Particularly, we have demonstrated that circulating exosomal microRNAs can be used as promising stable non-invasive biomarkers for early diagnosis of pancreatic cancer. Adviser: Juan Cui, Jitender Deogu